Opinions, design details, and engineering lessons from the builders of trained.chat.

Adding endless conversation history sounds great in startup pitch decks, but in production, it creates massive hallucination loops and spikes API costs. Here is the context architecture we built instead.
After auditing thousands of failed RAG queries, we threw out the standard tutorials. A detailed breakdown of parent-child overlaps and semantic node splitting.
How giving agents raw tool-calling powers backfired, and the design pattern we built to request human confirmation for sensitive billing webhooks.
An open postmortem on a bad prompts deployment, the debugging tools we used, and why we rebuilt our playground compare mode.
Lessons, mistakes, strategy, and architecture decisions from building in the open.
Retrieval systems, infrastructure, AI performance, and production reliability.
Why features shipped, why others were removed, and the trade-offs behind them.

How giving agents raw tool-calling powers backfired, and the design pattern we built to request human confirmation for sensitive billing webhooks.

Why generic lead gen chatbots annoy visitors, and how we designed custom interactive form cards that actually convert.
Support, onboarding, retention, conversation quality, and human handoff in production.

Too early, and you lose deflection. Too late, and you alienate customers. The exact mathematical scoring model we use to hand off active conversations.

A checklist of the data mismatches, prompt drifts, and compliance gaps that kill company agents once they touch live production traffic.
Interface, motion, typography, and systems.